Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

Peggy Peissig,Gerard Tromp,Kelsey M Schwei,Marc S Williams,Tamara R Vrabec,Murray H Brilliant,Jennifer Allen Pacheco,Joseph Finamore,Catherine A Mccarty,Joshua C Denny,Jyotishman Pathak,Efrain Cancel,Asha Okorie,Kate L Thomas,Stephen B Ellis,Luke V Rasmussen,Christopher Kadolph

doi:10.2196/medinform.7465

Abstract

BackgroundThe capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical.ObjectiveThe purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system.MethodsStakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information.ResultsThe context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data.ConclusionsThis paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities.

Highlights

BackgroundCapturing clinical information in a machine-interpretable format is challenging yet extremely critical to patient care, biomedical research, health care quality, and workflow efficiency initiatives [1,2,3,4,5,6,7]
The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities
Health care institutions often supplement electronic health records (EHRs) data capture functionality with vendor software solutions, form development tools, or standard office tools to create such capability [10,11,12]. These solutions usually have EHR integration limitations, are platform or device dependent, are difficult to maintain, lack security, and are limited in the ability to capture and use a broad range of clinical data capture methods. Not all of these solutions have exposed application program interfaces (APIs) that can be used to interface with EHRs, and there are challenges of maintaining functionality with the upgradation of the form system or EHR

Summary

Introduction

BackgroundCapturing clinical information in a machine-interpretable format is challenging yet extremely critical to patient care, biomedical research, health care quality, and workflow efficiency initiatives [1,2,3,4,5,6,7]. Health care institutions often supplement EHR data capture functionality with vendor software solutions, form development tools, or standard office tools (eg, spreadsheets and document editors) to create such capability [10,11,12]. These solutions usually have EHR integration limitations, are platform or device dependent, are difficult to maintain, lack security, and are limited in the ability to capture and use a broad range of clinical data capture methods. The importance of this activity for patient care and research is critical

Methods

Results

Discussion

Conclusion